Building rich and grounded robot world models from sensors and knowledge resources: A conceptual spaces approach

Robots interacting with other agents in rich information landscapes and complex dynamic physical environments require sophisticated and robust concept and knowledge management capabilities if they are to solve problems, communicate, learn and exhibit intelligent behaviours. In this paper we describe how conceptual spaces provide a powerful substrate upon which to build effective concept and knowledge management capabilities that integrate information from multiple sensory and symbolic sources. We use SONY AIBO robots and the robot soccer domain to illustrate our framework and approach. The conceptual spaces framework allows robots to build rich and grounded world models from a wide variety of internal and external knowledge resources, e.g.... (More)

Robots interacting with other agents in rich information landscapes and complex dynamic physical environments require sophisticated and robust concept and knowledge management capabilities if they are to solve problems, communicate, learn and exhibit intelligent behaviours. In this paper we describe how conceptual spaces provide a powerful substrate upon which to build effective concept and knowledge management capabilities that integrate information from multiple sensory and symbolic sources. We use SONY AIBO robots and the robot soccer domain to illustrate our framework and approach. The conceptual spaces framework allows robots to build rich and grounded world models from a wide variety of internal and external knowledge resources, e.g. sensors, ontologies, databases, knowledge bases, the semantic Web, Web services, and other agents. Conceptual spaces provide an important and effective bridge between the perceptual level and the symbolic level by grounding sensory information to objects (Less)